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A synthetic digital city dataset for robustness and generalisation of depth estimation models

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posted on 2024-04-19, 14:32 authored by Jihao LinJihao Lin, Jincheng Hu, Yanjun Huang, Zheng Chen, Bingzhao Gao, Jingjing JiangJingjing Jiang, Yuanjian ZhangYuanjian Zhang

Existing monocular depth estimation driving datasets are limited in the number of images and the diversity of driving conditions. The images of datasets are commonly in a low resolution and the depth maps are sparse. To overcome these limitations, we produce a Synthetic Digital City Dataset (SDCD) which was collected under 6 different weather driving conditions, and 6 common adverse perturbations caused by the data transmission. SDCD provides a total of 930K high-resolution RGB images and corresponding perfect observed depth maps. The evaluation shows that depth estimation models which are trained on SDCD provide a clearer, smoother, and more precise long-range depth estimation compared to those trained on one of the best-known driving datasets KITTI. Moreover, we provide a benchmark to investigate the performance of depth estimation models in different adverse driving conditions. 

Instead of collecting data from the real world, we generate the SDCD under severe driving conditions with perfect observed data in the digital world, enhancing depth estimation for autonomous driving.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Scientific Data

Volume

11

Publisher

Nature Portfolio

Version

  • VoR (Version of Record)

Rights holder

© The Author(s)

Publisher statement

This Open Access article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Acceptance date

2024-01-30

Publication date

2023-03-16

Copyright date

2024

eISSN

2052-4463

Language

  • en

Depositor

Dr Jingjing Jiang. Deposit date: 30 January 2024

Article number

301

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